The majority of uncertainty quantification methods for deep object detectors are based on the network output, such as sampling strategies like Monte-Carlo dropout or deep ensembles with straight-forward transfers to object detection. Here, we study gradient-based uncertainty features for object detection. We show that they contain information orthogonal to that of common, output-based uncertainty approximation methods. Meta classification and meta regression are used to produce confidence estimates using gradient features and other methods which are applicable to numerous object detection architectures. Our results show that gradient uncertainty itself performs on par with state-of-the-art methods across different detectors and datasets. We find that combined meta classifiers outperform standalone models. This suggests that sampling strategies may be supplemented by gradient-based uncertainty to obtain improved confidences, contributing to the probabilistic reliability of object detectors in down-stream applications.